A Blueprint for Real-Time Inattention Detection: A Modular Web Architecture with Transformer-Based Computer Vision

Astete L.F.P.; Cubillas J.D.S.; Silva P.D.R. · Proceedings - 2025 5th International Conference on Information Systems and Software Technologies, ICI2ST 2025 (2025) · DOI: 10.1109/ici2st68465.2025.00009

Resumen

Inattention in e-learning environments has emerged as a critical factor affecting student engagement and academic performance, particularly in the wake of widespread online education adoption. This paper proposes a modular and scalable architectural blueprint for a web-based application that enables real-time detection of student inattention levels using computer vision techniques and deep learning models. The design incorporates a facial expression analysis pipeline based on the Dataset for Affective States in E-Environments (DAiSEE) and considers two temporal modeling approaches: a Bi-directional long short-term memory (Bi-LSTM) and a Transformer-based architecture, for future implementation. A dual-phase validation plan is outlined to assess both the technical feasibility and educational relevance of the application, including quantitative model evaluation and qualitative feedback from instructors and students in real classroom settings. This proposal aims to provide a foundation for data-driven instructional support by enabling automated, real-time monitoring of inattention levels in online classes.

Instituciones: Universidad Peruana de Ciencias Aplicadas